Catalog quality management model

ABSTRACT

In one example, a content catalog system may process a bulk set of errors to prioritize those errors that may benefit from manual review by a human error administrator. A catalog quality management sub-system of the content catalog system may receive an error output describing a catalog error for a product aspect of a product in a content catalog from an error detection module. The catalog quality management sub-system may categorize the catalog error by a degree of human interaction with an error fix determined from an error metric in the error output. The catalog quality management sub-system may apply an error fix to the catalog error based on the degree of human interaction.

BACKGROUND

A content service may store a content catalog describing the contentavailable via the content service. The content catalog may be hosted ata content store accessible with a user device via a data network. Theuser device may review the content catalog and place an order for thecontent at the content store. For digital content, the user device maydownload the digital content via the data network. Alternately for hardcopy content, the content store may process the order and deliver thecontent to the user via other methods, such as the postal service.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that is further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Examples discussed below relate to processing bulk set of errors toprioritize those errors that may benefit from manual review by a humanerror administrator. A catalog quality management sub-system of thecontent catalog system may receive an error output describing a catalogerror for a product aspect of a product in a content catalog from anerror detection module. The catalog quality management sub-system maycategorize the catalog error by a degree of human interaction with anerror fix determined from an error metric in the error output. Thecatalog quality management sub-system may apply an error fix to thecatalog error based on the degree of human interaction.

DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionis set forth and will be rendered by reference to specific examplesthereof which are illustrated in the appended drawings. Understandingthat these drawings depict only typical examples and are not thereforeto be considered to be limiting of its scope, implementations will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings.

FIG. 1 illustrates, in a block diagram, one example of a contentnetwork.

FIG. 2 illustrates, in a block diagram, one example of a computingdevice.

FIG. 3 illustrates, in a block diagram, one example of a content catalogsystem.

FIG. 4 illustrates, in a flowchart, one example of a method foridentifying errors at the main platform of a content catalog system.

FIG. 5 illustrates, in a flowchart, one example of a method forprocessing errors at the catalog quality management system of a contentcatalog system.

FIG. 6 illustrates, in a block diagram, one example of an errordetection system.

FIG. 7 illustrates, in a flowchart, one example of a method foridentifying errors with an error detection system.

FIG. 8 illustrates, in a block diagram, one example of an error output.

FIG. 9 illustrates, in a flowchart, one example of a method foridentifying errors with an error detection module.

FIG. 10 illustrates, in a block diagram, one example of a productimportance module.

FIG. 11 illustrates, in a flowchart, one example of a method fordetermining a product importance.

FIG. 12 illustrates, in a block diagram, one example of an errorclassification module.

FIG. 13 illustrates, in a flowchart, one example of a method forclassifying error issues.

FIG. 14 illustrates, in a flowchart, one example of a method forreceiving an error decision from a human error administrator.

DETAILED DESCRIPTION

Examples are discussed in detail below. While specific implementationsare discussed, it should be understood that this is done forillustration purposes only. A person skilled in the relevant art willrecognize that other components and configurations may be used withoutparting from the spirit and scope of the subject matter of thisdisclosure. The implementations may be a catalog quality managementsub-system, a computing device, or a machine-implemented method.

In one example, a content catalog system may process bulk set of errorsto prioritize those errors that may benefit from manual review by ahuman error administrator. A human error administrator is a human taskedwith manually reviewing catalog errors. A catalog quality managementsub-system of the content catalog system may receive an error outputdescribing a catalog error for a product aspect of a product in acontent catalog from an error detection module. A product aspect may beany description of the product in the catalog as well as any contentdeliverables of the product itself. The catalog quality managementsub-system may categorize the catalog error by a degree of humaninteraction with an error fix determined from an error metric in theerror output. The catalog quality management sub-system may apply anerror fix to the catalog error based on the degree of human interaction.

A content delivery system may provide content on a fully automated basisfrom the content storage of the providers to the delivery to the enduser. With such a large amount of content available, errors in thecontent catalog may be inevitable. Further, with such a large set ofcontent, the inevitable errors may be more than humanly possible toreview in even the best run systems.

Each content store in a content catalog system may have an errordetection module producing an error output describing a catalog error ina standardized format for processing by a catalog quality managementsub-system. The catalog quality management sub-system may aggregate theerror outputs into an error output report. The catalog qualitymanagement sub-system may determine an impact state for the erroroutputs based on confidence in the identification of an error and theimpact of fixing or not fixing the error. The catalog quality managementsub-system may use the impact state to determine whether toautomatically fix the catalog error and whether to request a human erroradministrator to manually review the catalog error and the fix. Thecatalog quality management sub-system may determine an importance of thecatalog product affected by the error. The catalog quality managementsub-system may provide an error review list organized based on productimportance and impact state to the human error administrator.

FIG. 1 illustrates, in a block diagram, one example of a content network100. A user may use a user device 110 that executes a browser 112 toaccess a content store 120 via a data network connection 130. The userdevice 110 may be a desktop computer, a laptop computer, a tablet, asmart phone, or a dedicated digital audio player. The data networkconnection 130 may be an internet connection, a wide area networkconnection, a local area network connection, or other type of datanetwork connections.

The content store 120 may be a sales portal for content, such as audiofiles, video files, e-books, or other media. The user device 110 maydirectly download any purchased content or process sales requests andarrange for later delivery of content. The content store 120 may beimplemented on a single server or a distributed set of servers, such asa server farm. The content store 122 may store a catalog 122 fortransmission to the user device. The catalog 122 may list a descriptionof one or more products 124 available to the user device. The catalog122 may also detail a procedure for purchasing those products 124, aswell as describing any applicable sales terms.

FIG. 2 illustrates a block diagram of an exemplary computing device 200which may act as a content catalog system. The computing device 200 maycombine one or more of hardware, software, firmware, andsystem-on-a-chip technology to implement a content catalog system. Thecomputing device 200 may include a bus 210, a processing core 220, amemory 230, a data storage 240, a data interface 250, an input device260, an output device 270, and a communication interface 280. The bus210, or other component interconnection, may permit communication amongthe components of the computing device 200.

The processing core 220 may include at least one conventional processoror microprocessor that interprets and executes a set of instructions.The processing core 220 may be configured to implement a series ofinstructions for a catalog quality management sub-system. The processingcore 220 may be configured to categorize the catalog error by a degreeof human interaction with an error fix determined from an error metricin the error output. The error output may have a confidence scoredescribing a likelihood the catalog error is accurately identified. Theerror output may have at least one of a false positive impact scoredescribing a false identification impact and a false negative impactscore describing an impact of ignoring an accurate identification. Theprocessing core 220 may be configured to compute an impact state basedon at least one of a false positive impact score, a false negativeimpact score, and a confidence score. The processing core 220 may beconfigured to apply an error fix to the catalog error based on thedegree of human interaction. The processing core 220 may be configuredto compute a product importance score for the product based on at leastone of a rating metric describing a product quality of the product and atraffic metric describing consumption of the product. The processingcore 220 may be configured to compute an error priority of a catalogerror based on a product importance score for the product and an impactstate for the catalog error. The processing core 220 may be configuredto rank the catalog error in an error review list based on an errorpriority score. The processing core 220 may be configured to roll backthe error fix based on a rejection by a human error administrator.

The memory 230 may be a random access memory (RAM) or another type ofdynamic data storage that stores information and instructions forexecution by the processing core 220. The memory 230 may also storetemporary variables or other intermediate information used duringexecution of instructions by the processing core 220. The memory 230 maybe configured to store a series of instructions that are executed by atleast one processor to implement a catalog quality managementsub-system. The memory 230 may be configured to store an error logrecording the catalog error.

The data storage 240 may include a conventional ROM device or anothertype of static data storage that stores static information andinstructions for the processor 220. The data storage 240 may include anytype of tangible machine-readable medium, such as, for example, magneticor optical recording media, such as a digital video disk, and itscorresponding drive. A tangible machine-readable medium is a physicalmedium storing machine-readable code or instructions, as opposed to asignal. Having instructions stored on computer-readable media asdescribed herein is distinguishable from having instructions propagatedor transmitted, as the propagation transfers the instructions, versusstores the instructions such as can occur with a computer-readablemedium having instructions stored thereon. Therefore, unless otherwisenoted, references to computer-readable media/medium having instructionsstored thereon, in this or an analogous form, references tangible mediaon which data may be stored or retained. The data storage 240 may storea set of instructions detailing a method that when executed by one ormore processors cause the one or more processors to perform the method.The data storage 240 may also be a database or a database interface forstoring an error log.

The data interface 250 may be a hardware, software, or firmwareinterface designed to interact with an error detection module at acontent store. The data interface 250 may be configured to receive anerror output describing a catalog error for a product aspect of aproduct in a content catalog from an error detection module. The datainterface 250 may be configured to add the catalog error to a reportingexclusion list to prevent future reporting of the catalog error to ahuman error administrator.

The input device 260 may include one or more conventional mechanismsthat permit a user to input information to the computing device 200,such as a keyboard, a mouse, a voice recognition device, a microphone, aheadset, a touch screen 262, a touch pad 264, a gesture recognitiondevice 266, etc. The output device 270 may include one or moreconventional mechanisms that output information to the user, including adisplay screen 272, a printer, one or more speakers 274, a headset, avibrator, or a medium, such as a memory, or a magnetic or optical diskand a corresponding disk drive.

The communication interface 280 may include any transceiver-likemechanism that enables computing device 200 to communicate with otherdevices or networks. The communication interface 280 may include anetwork interface or a transceiver interface. The communicationinterface 280 may be a wireless, wired, or optical interface. Thecommunication interface 280 may be configured to receive a rating metricdescribing a product quality of the product from an external reviewsource. The communication interface 280 may be configured to request apost-fix manual review of the error fix by a human error administrator.The communication interface 280 may be configured to request a pre-fixmanual review of the error fix by a human error administrator.

The computing device 200 may perform such functions in response toprocessor 220 executing sequences of instructions contained in acomputer-readable medium, such as, for example, the memory 230, amagnetic disk, or an optical disk. Such instructions may be read intothe memory 230 from another computer-readable medium, such as the datastorage 240, or from a separate device via the communication interface280.

FIG. 3 illustrates, in a block diagram, one example of a content catalogsystem 300. The content catalog system 300 may have a main platform 310that organizes a set of content data 312 as input into a content catalog314 describing products placing the content into merchandisable form.The content data 312 may include content identifiers, metadatadescribing the content, media files representing the content,availability description of the content, stock keeping units uniquelyidentifying the products, and other data. The main platform 310 maypresent the content catalog 314 to a user via a front end module 316.The front end module 316 may collect a set of user metrics describinguser interactions with the content catalog 314 in a user metricsdatabase 318. The user metrics may include usage facts, clicks oncatalog products, product consumption, and other user interactiondescriptions.

The content catalog system 300 may have a catalog quality managementsub-system 320. The content catalog 314 may provide an error output 322identifying an error in a product aspect of the content catalog 314 tothe error processing module 330. A product aspect may be any descriptionof the product in the catalog as well as any content deliverables of theproduct itself. The catalog quality management sub-system 320 may havean error processing module 330 to correct the error identified in theerror output 322. The error processing module 330 may have an errorissue classifier 332 that factors user metrics from the user metricsdatabase 318 to determine the degree of human interaction to use infixing the error. The error issue classifier 332 may apply an automatedfix 334 to the error identified in the error output 322. The errorprocessing module 330 may update the content catalog 314 with theautomated fix 334. The error issue classifier 332 may create an errorlog 336 describing the error and the automated fix 334.

The error issue classifier 332 may determine that a human erroradministrator 340 is to review the error output 322. The error issueclassifier 332 may provide an error review list describing one or morecatalog errors to the human error administrator 340. The error issueclassifier 332 may prioritize the catalog errors in the error reviewlist to emphasize certain catalog errors to the human erroradministrator 340. The human error administrator 340 may apply a manualfix 342 to the catalog error. The human error administrator 340 mayupdate the content catalog 314 with the manual fix 342. The human erroradministrator 340 may create an error log 336 describing the error andthe manual fix 342.

FIG. 4 illustrates, in a flowchart, one example of a method 400 foridentifying errors at the main platform of a content catalog system. Thecontent catalog system may receive a set of raw product data as an input(Block 402). The content catalog system may store the product data in acontent catalog for presentation to a user (Block 404). The contentcatalog may interact with a user via a front end module (Block 406). Ifan error detection module detects a catalog error for a product in thecontent catalog (Block 408), the error detection module may provide anerror output describing a catalog error for a product aspect in acontent catalog from an error detection module (Block 410). The frontend module may report a set of usage data describing user interactions,such as a rating metric describing a user review of the product and atraffic metric describing consumption of the product (Block 412).

FIG. 5 illustrates, in a flowchart, one example of a method 500 forprocessing errors at the catalog quality management system of a contentcatalog system. An error processing module may receive a set of usagedata describing user interactions, such as a rating metric describing auser review of the product and a traffic metric describing consumptionof the product (Block 502). The error processing module may receive anerror output describing a catalog error for a product aspect of aproduct in a content catalog from an error detection module (Block 504).The error processing module may determine a degree of human interactionwith an error fix from an error metric in the error output (Block 506).If the error processing module determines that a human erroradministrator is to review the error fix for the catalog error (Block508), the error processing module may request a manual review of theerror fix by the human error administrator (Block 510). Otherwise, theerror processing module may apply an automatic error fix to the catalogerror based on the degree of human interaction (Block 512). The errorprocessing module may store an error log recording the catalog error aswell as the error fix (Block 514). The error processing module mayupdate the content catalog with the error fix (Block 516).

FIG. 6 illustrates, in a block diagram, one example of an errordetection system 600. Each content catalog 610 may have one or moreerror detection modules (EDM) 620. An error detection module 620 maycollect a list of issues impacting the content catalog 610. The errordetection module 620 may review the content catalog 610 for errorpatterns. The error detection module 620 may execute detection viaempirical checks using hardcoded rules of detection or machine learningalgorithms, such as language detection or outlier detection. Integratedinto a big data solution, an error detection module 620 may performchecks that allow for comparing a vast amount of products to each otherto look for near-duplicates or imitations.

Each error detection module 620 may produce a standardized individualerror output 622. The error detection module 620 may suggest anautomatic fix for the catalog error described in the individual erroroutput 622, such as correcting title misspellings and placing malwareinfected files in quarantine. An exclusion system 630 may compare eachindividual error output 622 to an exclusion list 632, removing anyindividual error output 622 identifying an issue that has already beenaddressed. An aggregator (AGG) 640 may aggregate the individual erroroutputs 622 into an aggregated error output report 642. By standardizingthe aggregated error outputs 642, an error processing system may compareand rank each individual error output 622 to prioritize catalog errorsfor manual fixes and manual reviews.

FIG. 7 illustrates, in a flowchart, one example of a method 700 foridentifying errors with an error detection system. An error detectionmodule may detect a catalog error for a product aspect in a contentcatalog (Block 702). The error detection module may generate anindividual error output describing the catalog error (Block 704). Theexclusion system may compare the individual error output to an exclusionlist (Block 706). If the individual error output matches a catalog erroron the exclusion list indicating the catalog error has been addressed(Block 708), the exclusion system may remove the individual error outputfrom the error collection pipeline (Block 710). Otherwise, the exclusionsystem may add the catalog error described in the individual erroroutput to the exclusion list (Block 712). An aggregator may aggregatethe individual error outputs (Block 714). The aggregator may generate anaggregated error output report (Block 716).

FIG. 8 illustrates, in a block diagram, one example of an error output800. The error output 800 may have data in at least one an issueidentifier category 810, an error metric category 820, a fix identifiercategory 830, a detail field category 840, and other data categories. Anissue identifier category 810 may include an identifier used to uniquelyidentify the issue. The issue identifier category 810 may have a productidentifier 812 indicating the product affected by the catalog error. Theissue identifier category 810 may have a marketplace identifier 814 thatidentifies where the product is being sold. The issue identifiercategory 810 may have an issue type field 816 describing a productaspect affected by the catalog error, such as the product title, productdescription, product deliverable, price, and other product features. Theissue identifier category 810 may have a sub-issue type field 818describing the catalog error, such as a typo, wrong descriptionlanguage, misquoted price, failure to apply a restriction, or othererror issues.

An error metric category 820 may include error metrics used to classifyan error and compute the priority of the error issue. An error metricmay measure the importance of an error issue and assess the risk of anautomatic fix. The error metric category may include a confidence score822, a false positive impact (FPI) score 824, a false negative impact(FNI) score 826, or other error metrics. A confidence score 822 is acomputed estimate score of the likelihood that the detection is a truepositive. A high score may mean that the error detection module has highconfidence that the detection is correct. A low score may mean the errordetection module has low confidence that the detection is correct. Afalse positive impact score 824 is a computed or hardcoded score toevaluate the impact of applying an automatic fix when the detection isin error. A false negative impact score 826 is a computed or hardcodedscore to evaluate the impact of not applying an automatic fix or amanual fix when the detection is accurate. For an image quality error,the error detection module may compute the false negative impact score826 by comparing the difference of the current image quality score and aminimum quality score.

A fix identifier category 830 may include an identifier used to describea suggested fix for the catalog error when possible. The fix identifiercategory 830 may have an old value field 832 describing the originaluncorrected value for the product aspect affected by the catalog error.The fix identifier category 830 may have a new value field 834describing the corrected value for the product aspect affected by thecatalog error after the fix has been applied. For example, for a titleerror wherein the title of the song “Purple Rain” is listed as “PupilRain”, the old value field 832 may list “Pupil Rain” and the new valuefield 834 may list “Purple Rain”.

A detail field category 840 may describe various information tofacilitate understanding of an issue during manual review. The detailfield category 840 may have a size field 842 describing the size of theproduct aspect affected by the error, such as a title length or filesize in bytes. The detail field category 840 may have a language (LANG)field 844 describing the language of the product aspect, such asidentifying the language for the title of the song “Raspberry Beret” asEnglish.

FIG. 9 illustrates, in a flowchart, one example of a method 900 foridentifying errors with an error detection module. The error detectionmodule may identify a catalog error for a product aspect of a product ina content catalog (Block 902). The error detection module may determinea confidence score describing a likelihood the catalog error isaccurately identified (Block 904). The error detection module maysuggest at least one of an automatic fix or a manual fix for the catalogerror (Block 906). The error detection module may calculate a falsepositive impact score describing a false identification impact (Block908). The error detection module may calculate a false negative impactscore describing an impact of ignoring an accurate identification (Block910). The error detection module may generate a detail descriptiondescribing a product aspect affected by the catalog error (Block 912).The error detection module may generate an individual error output foraggregation (Block 914).

FIG. 10 illustrates, in a block diagram, one example of a productimportance module 1000. The product importance module 1000 may calculatea product importance score using a variety of metrics. The productimportance module 1000 may access an internal telemetry data set 1002generated within the content catalog system. The internal telemetry dataset 1002 may have one or more rating metrics describing product quality,such as a user rating, product novelty, and other product reviews. Theinternal telemetry data set may have one or more traffic metricsdescribing consumption of the products, such as number of page views,number of purchases, predicted future views, and other interactivemetrics. Further, the product importance module 1000 may receive one ormore rating metrics describing a product quality of the product from anexternal review source 1004, such as partner data and expert reviews.

A product importance (PI) computational module 1006 may combine thevarious metrics from the internal telemetry data set 1002 and theexternal review source 1004 to calculate a product importance score. Theproduct importance computational module 1006 may compute the productimportance score using the following formula:PI=Σ_(i=1) ^(N)α_(i) s _(i),where N is the number of metric sub-scores, s_(i) is the sub-score forthe metric, and a_(i) is a sub-score coefficient parameter. The productimportance computational model 1006 may use the sub-score coefficientparameter to weight one metric sub-score in comparison to a differentmetric sub-score. For example, the product importance computationalmodule may value one purchase more than fifty page views.

Further, the metric sub-score s_(i) may be dependent on whether themetric is a traffic metric or a rating metric. The product importancecomputational module 1006 may calculate a traffic metric sub-score usingthe following formula:s _((i|traffic))=Σ_(j=1) ^(n)exp^(−β) ^(j) ^(T) ^(j) ,where n is the number of traffic events related to the productconsidered, T_(j) is the number of days ago the traffic event occurredon the product, and β_(j) is the decay coefficient. The productimportance computational module 1006 may apply the decay coefficient togive greater weight to more recent traffic events over older trafficevents. The product importance computational module 1006 may calculate arating metric sub-score using the following formula:

${s_{({i❘{rating}})} = \frac{r - \frac{r_{\max}}{2}}{r_{\max}}},$where r is the rating of the product and r_(max) is the maximum possiblerating.

The product importance module 1000 may combine the product importancescore with an error output report 1008 aggregated from error detectionsystem using a joining module 1010 to generate a pre-classification list1012. The pre-classification list 1012 may have separate fieldsdescribing the error output affecting a product aspect and the productimportance score for that product. The product importance module 1000may then provide the pre-classification list 1012 to an errorclassification module.

FIG. 11 illustrates, in a flowchart, one example of a method 1100 fordetermining a product importance score. The product importance modulemay access an internal telemetry data set generated within the contentcatalog system (Block 1102). The product importance module may receive arating metric describing a product quality of the product from anexternal review source (Block 1104). The product importance module maycompute a product importance for the product based on at least one of arating metric describing a product quality of the product and a trafficmetric describing consumption of the product (Block 1106). The productimportance module may receive an error output report aggregated from aset of error detection modules (Block 1108). The product importancemodule may join the product importance scores to an error output reportto generate a pre-classification list (Block 1110). The productimportance module may provide the pre-classification list to the errorclassification module (Block 1112).

FIG. 12 illustrates, in a block diagram, one example of an errorclassification module 1200. The error classification module 1200 mayfeed the pre-classification list 1202 to an issue classifier 1204. Theissue classifier 1204 may classify a catalog error into one of multipleimpact states. In state one (s₁) 1206, the error classification module1200 may apply an automatic fix 1208 to the catalog error, updating thecontent catalog 1210. The error classification module 1200 may notifyany external partners 1212 associated with the affected product. Theerror classification module 1200 may generate and store an error log1214 recording the catalog error. The error log 1214 may record theerror output, the old value of the affect product aspect, the new valueof the fixed product aspect, the date the automatic fix 1208 wasapplied, and the state of the catalog error.

In state two (s₂) 1216, the error classification module 1200 may applyan automatic fix 1208 to the catalog error, updating the content catalog1210. The error classification module 1200 may notify any externalpartners 1212 associated with the affected product. The errorclassification module 1200 may generate and store an error log 1214recording the catalog error. Additionally, the error classificationmodule 1200 may apply a ranker 1218 to compute the priority of thecatalog error. The ranker 1218 generates a review list 1220 with eachcatalog error ordered based on the priority for examination by a humanerror administrator. If the human error administrator disapproves of theautomatic fix 1208, the error classification module 1200 may roll backthe automatic fix 1208 returning the catalog error to an originalcondition. The error classification module 1200 may update the exclusionlist to the remove the catalog error from the system.

In state three (s₃) 1222, the error classification module 1200 may applya ranker 1218 to compute the priority of the catalog error. The ranker1218 generates a review list 1220 with each catalog error ordered basedon the priority for examination by a human error administrator. If thehuman error administrator approves of the suggested fix, the errorclassification module 1200 may apply the suggested fix to the catalogerror. Otherwise, the error classification module 1200 may update theexclusion list to the remove the catalog error from the system.

The issue classifier 1204 may use the confidence score, the falsepositive impact score, and the false negative impact score in thepre-classification list 1202 to classify the catalog error. If theconfidence score is high, most error detections may be a true positive.By applying the automatic error fix, the error classification module1200 may invert the error rate. If the confidence score is low, mosterror detections may be a false positive. The error classificationmodule 1200 may seek a manual check before applying a suggested fix. Anyconfidence score over fifty percent may reduce the error rate byapplying the automatic fixes 1208.

Generally, the issue classifier 1204 may consider the three scores in avariety of combinations. If the confidence score is high and the impactfor a mistake is low, represented by a low false positive impact score,the error detection module 1200 may apply an automatic fix, as in stateone 1206. If the confidence score is high but the impact for a mistakeis high, represented by a high false positive impact score, the errordetection module 1200 may apply an automatic fix to invert the errorrate but also request a follow up manual check, as in state two 1216. Ifthe confidence score is high, the impact for a mistake is low, and theimpact of doing nothing is high, represented by a high false negativeimpact score, the error detection module 1200 may apply an automaticfix, as in state one 1206. If the confidence score is high, the impactfor a mistake is high, and the impact of doing nothing is high, theerror detection module 1200 may apply an automatic fix to invert theerror rate but also request a follow up manual check, as in state two1216.

If the confidence score is low and the impact for doing nothing is low,the error detection module 1200 may request a manual check or donothing, as in state three 1222. If the confidence score is low, theimpact for a mistake is low, and the impact for doing nothing is high,the error detection module 1200 may apply an automatic fix to invert theerror rate but also request a follow up manual check, as in state two1216. If the confidence score is low, the impact for a mistake is high,and the impact for doing nothing is high, the error detection module1200 may request a manual check before acting, as in state three 1222.

In a two state system, the issue classifier 1204 may classify a catalogerror as being state one 1206 if the confidence score is greater thanfifty percent and the false positive impact score is less than the falsenegative score. Thus, the error classification module 1200 may apply anautomatic fix 1208. Otherwise, the issue classifier 1204 may classify acatalog error as state three 1222, as hybrid state two 1216 does notexist in this scenario. The error classification module may include thecatalog error in the prioritized review list 1220 for presentation to ahuman error administrator for manual review.

In a three state system, the issue classifier 1204 may classify acatalog error as being state one 1206 if the confidence score is greaterthan eighty percent, the false positive impact score is less than twentypercent, and the false negative score is greater than eighty percent.Thus, the error classification module 1200 may apply an automatic fix1208. Otherwise, the issue classifier 1204 may classify the catalogerror as state two 1216 if the confidence score is greater than fiftypercent, the false positive impact score is less than forty percent, andthe false negative score is greater than sixty percent. Thus, the errorclassification module 1200 may apply an automatic fix 1208 and includethe catalog error in the prioritized review list 1220 for presentationto a human error administrator for manual review. Otherwise, the issueclassifier 1204 may classify a catalog error as state three 1222. Theerror classification module 1200 may include the catalog error in theprioritized review list 1220 for presentation to a human erroradministrator for manual review.

The ranker 1218 may compute a priority score for issues classified asstate two 1216 or state three 1222. A high priority score may indicatethat the catalog error is significant and may be ranked higher forearlier review. If the state is state three 1222, the errorclassification module 1200 may decide whether to apply the fix. Theranker 1218 may use the false negative impact score coupled with theconfidence score and the product importance score to prioritize thecatalog error. The priority score may equal the product importance scoretimes the confidence score times the false negative impact score. If thestate is state two 1216, the error classification module 1200 hasapplied the fix and the fix may be reviewed manually. The ranker 1218may use the false positive impact score coupled with the inverse of theconfidence score and the product importance score to prioritize thecatalog error. The inverse of the confidence score, representing theprobability that correcting the catalog error was a mistake, may equalone minus the confidence score. The priority score may equal the productimportance score times the inverse confidence score times the falsepositive impact score.

FIG. 13 illustrates, in a flowchart, one example of a method 1300 forclassifying error issues. An error classification module may receive apre-classification list containing an error output and a productimportance score (Block 1302). The error output may list a catalog errorfor a product aspect of a product in a content catalog from an errordetection module, a confidence score describing a likelihood the catalogerror is accurately identified, a false positive impact score describinga false identification impact, and a false negative impact scoredescribing an impact of ignoring an accurate identification. The errorclassification module may compute an impact state based on at least oneof a false positive impact score, a false negative impact score, and aconfidence score (Block 1304). The error classification module maycategorize the catalog error by a degree of human interaction with anerror fix determined from an error metric in the error output, such asthe impact state (Block 1306).

If the catalog error is classified in an impact state incorporating anautomatic fix (Block 1308), the error classification module may apply anerror fix to the catalog error based on the degree of human interaction(Block 1310). The error classification module may notify an externalpartner associated with the product that the automatic fix has beenapplied (Block 1312). The error classification module may store an errorlog recording the catalog error and the automatic fix (Block 1314). Theerror classification module may update the content catalog at thecontent store (Block 1316).

If the catalog error is classified in an impact state incorporating anautomatic fix (Block 1308) and a manual review (Block 1318), the errorclassification module may compute an error priority score of a catalogerror based on a product importance score for the product and an impactstate for the catalog error (Block 1320). The error classificationmodule may rank the catalog error in an error review list based on anerror priority score (Block 1322). The error classification module mayrequest a post-fix manual review of the error fix by a human erroradministrator (Block 1324).

If the catalog error is classified in an impact state incorporating justa manual review (Block 1318), the error classification module maycompute an error priority of a catalog error based on a productimportance for the product and an impact state for the catalog error(Block 1320). The error classification module may rank the catalog errorin an error review list based on an error priority score (Block 1322).The error classification module may request a pre-fix manual review ofthe error fix by a human error administrator (Block 1324).

FIG. 14 illustrates, in a flowchart, one example of a method 1400 forreceiving an error decision from a human error administrator. The errorclassification module may present the error review list to a human erroradministrator (Block 1402). The error classification module may receivea review input from the human error administrator indicating a fixapproval or disapproval (Block 1404). The error classification modulemay determine the impact state of the catalog error (Block 1406). If thecatalog error has an impact state of state three (Block 1408) and thehuman error administrator has approved the suggested fix (Block 1410),the error classification module may apply the suggested fix to thecatalog error (Block 1412). If the catalog error has an impact stateother than state three (Block 1408) and the human error administratorhas disapproved the suggested fix (Block 1414), the error classificationmodule may roll back the error fix based on a rejection by a human erroradministrator (Block 1416).

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms for implementing the claims.

Examples within the scope of the present invention may also includecomputer-readable storage media for carrying or havingcomputer-executable instructions or data structures stored thereon. Suchcomputer-readable storage media may be any available media that can beaccessed by a general purpose or special purpose computer. By way ofexample, and not limitation, such computer-readable storage media cancomprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic data storages, or any othermedium which can be used to store desired program code means in the formof computer-executable instructions or data structures, as opposed topropagating media such as a signal or carrier wave. Computer-readablestorage media explicitly does not refer to such propagating media.Combinations of the above should also be included within the scope ofthe computer-readable storage media.

Examples may also be practiced in distributed computing environmentswhere tasks are performed by local and remote processing devices thatare linked (either by hardwired links, wireless links, or by acombination thereof) through a communications network.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, and data structures, etc. that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described examples are part of the scope of the disclosure. Forexample, the principles of the disclosure may be applied to eachindividual user where each user may individually deploy such a system.This enables each user to utilize the benefits of the disclosure even ifany one of a large number of possible applications do not use thefunctionality described herein. Multiple instances of electronic deviceseach may process the content in various possible ways. Implementationsare not necessarily in one system used by all end users. Accordingly,the appended claims and their legal equivalents should only define theinvention, rather than any specific examples given.

We claim:
 1. A catalog quality management sub-system, comprising: a data interface configured to receive an error output describing a catalog error for a product aspect of a product in a content catalog from an error detection module; a processing core having at least one processor configured to categorize the catalog error by a degree of human interaction with an error fix determined from an error metric in the error output and configured to apply an error fix to the catalog error based on the degree of human interaction; and memory configured to store an error log recording the catalog error.
 2. The catalog quality management sub-system of claim 1, wherein the catalog error is added to a reporting exclusion list to prevent future reporting of the catalog error to a human error administrator.
 3. The catalog quality management sub-system of claim 1, wherein the error output has a confidence score describing a likelihood the catalog error is accurately identified.
 4. The catalog quality management sub-system of claim 1, wherein the error output has at least one of a false positive impact score describing a false identification impact and a false negative impact score describing an impact of ignoring an accurate identification.
 5. The catalog quality management sub-system of claim 1, wherein the processing core is configured to compute an impact state based on at least one of a false positive impact score, a false negative impact score, and a confidence score.
 6. The catalog quality management sub-system of claim 1, further comprising: a communication interface is configured to receive a rating metric describing a product quality of the product from an external review source.
 7. The catalog quality management sub-system of claim 1, wherein the processing core is configured to compute a product importance score for the product based on at least one of a rating metric describing a product quality of the product and a traffic metric describing consumption of the product.
 8. The catalog quality management sub-system of claim 1, wherein the processing core is configured to compute an error priority of a catalog error based on a product importance score for the product and an impact state for the catalog error.
 9. The catalog quality management sub-system of claim 1, wherein the processing core is configured to rank the catalog error in an error review list based on an error priority score.
 10. The catalog quality management sub-system of claim 1, further comprising: a communication interface is configured to request a post-fix manual review of the error fix by a human error administrator.
 11. The catalog quality management sub-system of claim 1, wherein the processing core is configured to roll back the error fix based on a rejection by a human error administrator.
 12. The catalog quality management sub-system of claim 1, further comprising: a communication interface is configured to request a pre-fix manual review of the error fix by a human error administrator.
 13. A computing device, having a memory to store a series of instructions that are executed by at least one processor to implement a catalog quality management sub-system, the computing device configured to receive an error output describing a catalog error for a product aspect of a product in a content catalog from an error detection module; compute an impact state based on at least one of a false positive impact score describing a false identification impact, a false negative impact score describing an impact of ignoring an accurate identification, and a confidence score describing a likelihood the catalog error is accurately identified; categorize the catalog error by a degree of human interaction with an error fix determined from an impact state; and apply an error fix to the catalog error based on the degree of human interaction.
 14. The computing device of claim 13, wherein the computing device is further configured to receive a rating metric describing a product quality of the product from an external review source.
 15. The computing device of claim 13, wherein the computing device is further configured to compute a product importance score for the product based on at least one of a rating metric describing a product quality of the product and a traffic metric describing consumption of the product.
 16. The computing device of claim 13, wherein the computing device is further configured to compute an error priority score of a catalog error based on a product importance score for the product and an impact state for the catalog error.
 17. The computing device of claim 13, wherein the computing device is further configured to rank the catalog error in an error review list based on an error priority score.
 18. The computing device of claim 13, wherein the computing device is further configured to roll back the error fix based on a rejection by a human error administrator.
 19. A machine-implemented method, comprising: receiving an error output describing a catalog error for a product aspect of a product in a content catalog from an error detection module; computing an impact state based on at least one of a false positive impact score describing a false identification impact, a false negative impact score describing an impact of ignoring an accurate identification, and a confidence score describing a likelihood the catalog error is accurately identified; categorizing the catalog error by a degree of human interaction with an error fix determined from an impact state; applying an error fix to the catalog error based on the degree of human interaction; and requesting a post-fix manual review of the error fix by a human error administrator.
 20. The method of claim 19, further comprising: computing a product importance score for the product based on at least one of a rating metric describing a product quality of the product and a traffic metric describing consumption of the product; computing an error priority score of a catalog error based on a product importance score for the product and an impact state for the catalog error; and ranking the catalog error in an error review list based on an error priority score. 